{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 1.import工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import packages\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "train = pd.read_csv(\"pima-indians-diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 2.读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"pima-indians-diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Train :', (768, 9))\n"
     ]
    }
   ],
   "source": [
    "print(\"Train :\", train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 3.查看每个变量的分布及其与标签之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3.1 Target\n",
    "sns.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3.2 怀孕次数pregnants\n",
    "fig = plt.figure()\n",
    "### Number of occurrences\n",
    "sns.countplot(train['pregnants'])\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118c41b50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"pregnants\", hue=\"Target\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.3 血浆葡萄糖浓度Plasma_glucose_concentration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ys/anaconda3/lib/python2.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Plasma_glucose_concentration, kde = False)\n",
    "plt.xlabel('Plasma_glucose_concentration')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Plasma_glucose_concentration', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Plasma_glucose_concentration', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.4 血压blood_pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.blood_pressure, kde = False)\n",
    "plt.xlabel('blood_pressure')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='blood_pressure', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('blood_pressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.5 三头肌皮褶厚度 Triceps_skin_fold_thickness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Triceps_skin_fold_thickness, kde = False)\n",
    "plt.xlabel('Triceps_skin_fold_thickness')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Triceps_skin_fold_thickness', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Triceps_skin_fold_thickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.6 餐后血清胰岛素serum_insulin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.serum_insulin, kde = False)\n",
    "plt.xlabel('serum_insulin')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='serum_insulin', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('serum_insulin', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.7 体重指数BMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.BMI, kde = False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='BMI', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1199cab50>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF = train.groupby(['BMI', 'Target'])['BMI'].count().unstack('Target').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.8 糖尿病家系作用Diabetes_pedigree_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Diabetes_pedigree_function, kde = False)\n",
    "plt.xlabel('Diabetes_pedigree_function')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119f1b710>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Diabetes_pedigree_function', 'Target'])['Diabetes_pedigree_function'].count().unstack('Target').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.9 年龄Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Age, kde = False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a853410>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Age', 'Target'])['Age'].count().unstack('Target').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 4. 特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b5fead0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tcBZg0GskSzVctBShu9jb6huH9jSuoF8NPDg0vQ04eUyvJS2ag+nziKW4Smqp/ss5mLZ1b5bsw9gkG4GNbfLnSe6d56qOBn60OL3qkvtnZt3tnzcv7rqWZP8s5jaM+TUXvH8WuK3/YJRG4wr67cDaoek1re5pVXUVcNVCXyjJlqqaXOh6euX+mZn7Z2bun5ktl/0zruvovw6sT3JskmcB5wCbxvRakqQZjOWMvqp2JXkn8CUGl1d+vKruGsdrSZJmNrYx+qq6EbhxXOsfsuDhn865f2bm/pmZ+2dmy2L/pKqWug+SpDHyXjeS1LllHfRJzkhyb5KpJBcudX+WQpK1SW5NcneSu5Jc0OqPSnJTku+15yNbfZJc0fbZHUlOXNotGL8kK5J8M8kNbfrYJJvbPvhsu2CAJIe26ak2f91S9nt/SXJEks8l+U6Se5Kc6vEzkOQ/tL+rO5N8Jsmzl+Pxs2yDfug2C68BjgPelOS4pe3VktgFvKeqjgNOAc5v++FC4OaqWg/c3KZhsL/Wt8dG4Mr93+X97gLgnqHpS4HLqurFwCPAea3+POCRVn9Za3cwuBz4YlW9BDiewb466I+fJKuBfw9MVtXLGFxYcg7L8fipqmX5AE4FvjQ0fRFw0VL3a6kfwPUM7jF0L7Cq1a0C7m3lPwPeNNT+6XY9Phh8h+Nm4DTgBiAMvuCycs/jiMFVYqe28srWLku9DWPeP4cD9++5nR4/Bc98w/+odjzcALx6OR4/y/aMnr3fZmH1EvXlgND+VTwB2AwcU1U72qyHgGNa+WDbbx8B/gB4qk0/D3i0qna16eHtf3rftPmPtfY9OxaYBv68DW99LMlhePxQVduBPwF+COxgcDxsZRkeP8s56DUkyXOAzwPvqqqfDs+rwSnGQXd5VZLXAjurautS9+UAthI4Ebiyqk4AfsEzwzTAQX38HMngZozHAs8HDgPOWNJOzdNyDvpZb7NwsEhyCIOQ/1RVfaFVP5xkVZu/CtjZ6g+m/fYK4HVJHgCuZTB8czlwRJLd3yEZ3v6n902bfzjw4/3Z4SWwDdhWVZvb9OcYBL/HD/wOcH9VTVfVE8AXGBxTy+74Wc5B720WGFwFAVwN3FNVHx6atQk4t5XPZTB2v7v+re3qiVOAx4b+Re9KVV1UVWuqah2D4+OWqnozcCtwdmu2577Zvc/Obu27PpOtqoeAB5P8w1Z1OoPbiR/0xw+DIZtTkvy99ne2e98sv+NnqT8kWOCHJWcC3wW+D7xvqfuzRPvgtxj8W30HcHt7nMlgbPBm4HvA3wBHtfZhcLXS94FvM7iiYMm3Yz/sp1cCN7TyC4HbgCngL4FDW/2z2/RUm//Cpe73fto3G4At7Rj6H8CRHj9P75sPAN8B7gT+Ajh0OR4/fjNWkjq3nIduJEkjMOglqXMGvSR1zqCXpM4Z9JLUOYNekjpn0EsjSPKOJG9d5HV+IsnZrfyxg/Tuq9oPxvZTgtJSSLKynrnh1KKpqj9d7HXusf5/Pc716+DmGb0OSEkOS/I/k3yr/ejDv0ry8iT/O8nWJF8auhfLl5N8JMkW4ILhM+U2/+ft+ZVt+euT3Jfkg0nenOS2JN9O8qIZ+nNxkt8fer1L23LfTfLPWv1LW93t7Uc51idZl+TOofX8fpKL97L+LyeZ3N3fJJe0bf9akmP2bC/NhUGvA9UZwP+pquNr8KMPXwT+C3B2Vb0c+DhwyVD7Z1XVZFV9aJb1Hg+8A/hHwFuA36yqk4CPAf9uDv1b2ZZ7F/D+VvcO4PKq2gBMMrhh2HwcBnytqo4HvgL8m3muRwIcutGB69vAh5JcyuAHHx4BXgbcNLi/FCsY3CN8t8+OuN6vV7sJV5LvA3899Hq/PYf+7b5L6FZgXSv/LfC+JGuAL1TV91pf5+r/Mtjm3et/1XxWIu1m0OuAVFXfbb9HeibwR8AtwF1Vdeo+FvnFUHkX7b/VJL8GPGto3uND5aeGpp9ibn8Pu5d7cvdyVfXpJJuBfwHcmOTtDG66N/yf87NHWPcT9cxNqJ5evzRfDt3ogJTk+cAvq+q/A38MnAxMJDm1zT8kyUv3sfgDwMtb+XXAIWPuLq1PLwTuq6orGNy69p8ADwO/keR5SQ4FXrs/+iIN80xBB6p/DPxxkqeAJ4B/y+BM/YokhzM4dj8C3LWXZT8KXJ/kWwzG9n+xlzbj8EbgLUmeYPDze/+5qp5I8p8Y3LZ2O4Nb3kr7lbcplqTOOXQjSZ1z6EYakuR9wBv2qP7Lqrpkb+2l5cChG0nqnEM3ktQ5g16SOmfQS1LnDHpJ6pxBL0md+3/LlYAdrX5lJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lwF8m+eCs7Y8CfwDsA+4fQj5pIJx+0VGnqv4RWA1MzFr3fWA7cCu9q/pJY8mRuo46Sc4BVtC7eNLxszb9CfB3VfUfvQtYSuPHUtfR4sCcOvS+Rm59Vb07u7yr6jk860Vjzqs0SlJDnFOXpIZY6pLUEEtdkhpiqUtSQyx1SWqIpS5JDbHUJakhlrokNeR/AQkZ889psPc0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ofKHdS90x6HVca9dSOo/B5WIvbW1PS/LBdm33nUluTnJxW3ZWkn9vF0K7ZfbUfGmSGfQ63m1mcB33/wK+l+Qs4PeAdcAG4I0Mrks0e+2lvwcurqqzgKuBK8dRtLQYI/txcGlKbGFwYTYYnJq+hcH74pNV9XPggSS3tuVnAC8Cdg4uX8IJDC5BK000g17HrSQrGFyB89eTFIPgLh6/Bs6THgLcWVUvPUYlSkPh0I2OZxcD/1JVz6uqdVW1lsEvNT0E/H4bq18FnN/W3wvMJHlsKCfJC8dRuLQYBr2OZ1t48t77p4FfYXCN97uAf2Xw048PV9XPGHw4vDfJ14Dbgd88duVKS+PVK6UjSPKsqvpxu0Tul4CXteu+S1PHMXrpyG5qPxbzDOCvDHlNM/foJalzjtFLUucMeknqnEEvSZ0z6CWpcwa9JHXOoJekzv0fyw3wLeVWmWkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NOee/Bg4B/rG973xHVZ06saIXacg5d2XIOX8GeGGSm4HvA39aVcv2p9Uh5/w64P1JXsPgjdmXL+cTtyQfZvCf9cr2vsMbgccAVNV7GbwPcQowAzwInD3S4y/jPztJ0hCWy6UbSdICGfSS1DmDXpI6Z9BLUucMeknq3LK4vVJaqCRHAjva5s8wuD3x4fuxT2hrrYz6mMczWHjs06N+bWkhDHp1rd1vfhxAkjcBD1TV3wz7/CQHVdX353nY4xl8gteg16OCl250wEryz0muaWu8/2HrW5HkviTvTHI9cEKSU9va6dckeVeST7SxhyT5YJIvtHXTfzfJ44E3AC9Ncl2S0yc4RQnwjF4Htk1VdU+SJwDTST4GfIvBOklXVdWr276vAM9h8LH8S2Y9/w3Ap6vq5UmOAHYCvwq8BXh6Vb16KScj7Y9n9DqQvSbJl4DPM1hE6udb//eAj7f2scCXq+r29hH8D896/guB1ye5DriSwXosxyxJ5dI8eEavA1Jb9fO5wIaq+naSzzEIaoBvD7muSoDTquprj3jt5462WmlxPKPXgeow4J4W8r8M/Np+xt0M/EKSNe03lv3+rH2fYdYvxGiricLg8s+hY6hZWhCDXgeqTwFPaCtCvpXB9fUf035V4yuBfwOmgfsY/PYygDcDT0xyQ5KbGPxWJIArgGe0N2h9M1YT5+qV0hySHFJVD7Qz+vcBN1TVuyZdlzQsz+ilub2iveF6M/B44P0TrkeaF8/oJalzntFLUucMeknqnEEvSZ0z6CWpcwa9JHXOoJekzv0fEWk6Z7SpO5MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in train.columns:\n",
    "    sns.distplot(train[feature],kde = False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
